Human visual behavior has an important potential for human activity cognition and analysis. With the maturing and price reduction of head-mounted eye-tracking software and hardware, as well as the popularity of Augmented Reality (AR) and Virtual Reality (VR) technologies, its applications have rapidly advanced from military and industrial applications in the past to consumer and entertainment applications. The application scenarios are also expanding from relatively fixed places, such as design rooms and laboratories, etc., to places in people's daily lives, and more and more mobile application scenarios emerged, such as games, education, and so on. In the near future, it may be widely used in mobile phones and glasses.
Thus, it can be valuable in the scientific and commercial fields to understand how to use the head-mounted eye-tracking technology to better collect, evaluate, and analyze human behavior, especially to collect certain hidden user status.
However, currently, visual activity information obtained by short-term eye tracking is used to judge user activity, manual classification and management is often required, and the efficiency and accuracy is also low.
The disclosed processor and information processing method thereof are directed to solve one or more problems set forth above and other problems.